Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations113998
Missing cells16
Missing cells (%)< 0.1%
Duplicate rows444
Duplicate rows (%)0.4%
Total size in memory17.5 MiB
Average record size in memory161.0 B

Variable types

Text5
Numeric12
Boolean1
Categorical2

Alerts

Dataset has 444 (0.4%) duplicate rowsDuplicates
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
energy is highly overall correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly overall correlated with acousticness and 1 other fieldsHigh correlation
explicit is highly imbalanced (57.9%) Imbalance
time_signature is highly imbalanced (73.9%) Imbalance
popularity has 16020 (14.1%) zeros Zeros
key has 13060 (11.5%) zeros Zeros
instrumentalness has 38761 (34.0%) zeros Zeros

Reproduction

Analysis started2025-04-29 13:26:08.654354
Analysis finished2025-04-29 13:27:00.722229
Duration52.07 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct89739
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2025-04-29T14:27:01.477585image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2507956
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73098 ?
Unique (%)64.1%

Sample

1st row5SuOikwiRyPMVoIQDJUgSV
2nd row4qPNDBW1i3p13qLCt0Ki3A
3rd row1iJBSr7s7jYXzM8EGcbK5b
4th row6lfxq3CG4xtTiEg7opyCyx
5th row5vjLSffimiIP26QG5WcN2K
ValueCountFrequency (%)
6s3jldagk3uu3ntzbpnuhs 9
 
< 0.1%
2ey6v4sekh3z0rusisrosd 8
 
< 0.1%
2kkvb3rnrzwjfdghaua0tz 8
 
< 0.1%
08kta3sl9sv6iy8klktgql 7
 
< 0.1%
6bzwr3epseolvwlblk58il 7
 
< 0.1%
5sqkarfxe7uejhtlcthcls 7
 
< 0.1%
1gqpa08t7ebavpqj9o9l2q 7
 
< 0.1%
0e5lcanke0uyjuucoq1uh2 7
 
< 0.1%
2aaclnypaakdamlw74jxxb 7
 
< 0.1%
4aqs25f3ywj9tgnnkoqilc 7
 
< 0.1%
Other values (89729) 113924
99.9%
2025-04-29T14:27:02.204352image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 53777
 
2.1%
5 53497
 
2.1%
2 53334
 
2.1%
6 53272
 
2.1%
0 53232
 
2.1%
1 53162
 
2.1%
4 53148
 
2.1%
7 50535
 
2.0%
K 39217
 
1.6%
D 39104
 
1.6%
Other values (52) 2005678
80.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2507956
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 53777
 
2.1%
5 53497
 
2.1%
2 53334
 
2.1%
6 53272
 
2.1%
0 53232
 
2.1%
1 53162
 
2.1%
4 53148
 
2.1%
7 50535
 
2.0%
K 39217
 
1.6%
D 39104
 
1.6%
Other values (52) 2005678
80.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2507956
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 53777
 
2.1%
5 53497
 
2.1%
2 53334
 
2.1%
6 53272
 
2.1%
0 53232
 
2.1%
1 53162
 
2.1%
4 53148
 
2.1%
7 50535
 
2.0%
K 39217
 
1.6%
D 39104
 
1.6%
Other values (52) 2005678
80.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2507956
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 53777
 
2.1%
5 53497
 
2.1%
2 53334
 
2.1%
6 53272
 
2.1%
0 53232
 
2.1%
1 53162
 
2.1%
4 53148
 
2.1%
7 50535
 
2.0%
K 39217
 
1.6%
D 39104
 
1.6%
Other values (52) 2005678
80.0%
Distinct31437
Distinct (%)27.6%
Missing1
Missing (%)< 0.1%
Memory size1.7 MiB
2025-04-29T14:27:02.809105image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length513
Median length322
Mean length16.319219
Min length2

Characters and Unicode

Total characters1860342
Distinct characters712
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16768 ?
Unique (%)14.7%

Sample

1st rowGen Hoshino
2nd rowBen Woodward
3rd rowIngrid Michaelson;ZAYN
4th rowKina Grannis
5th rowChord Overstreet
ValueCountFrequency (%)
the 6831
 
2.6%
3126
 
1.2%
de 1133
 
0.4%
los 1066
 
0.4%
of 1034
 
0.4%
dj 738
 
0.3%
george 593
 
0.2%
jones 524
 
0.2%
la 518
 
0.2%
for 457
 
0.2%
Other values (42276) 241838
93.8%
2025-04-29T14:27:03.997382image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 164224
 
8.8%
e 148727
 
8.0%
143869
 
7.7%
i 112148
 
6.0%
n 106547
 
5.7%
o 103829
 
5.6%
r 100224
 
5.4%
l 75685
 
4.1%
s 69312
 
3.7%
t 63611
 
3.4%
Other values (702) 772166
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1860342
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 164224
 
8.8%
e 148727
 
8.0%
143869
 
7.7%
i 112148
 
6.0%
n 106547
 
5.7%
o 103829
 
5.6%
r 100224
 
5.4%
l 75685
 
4.1%
s 69312
 
3.7%
t 63611
 
3.4%
Other values (702) 772166
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1860342
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 164224
 
8.8%
e 148727
 
8.0%
143869
 
7.7%
i 112148
 
6.0%
n 106547
 
5.7%
o 103829
 
5.6%
r 100224
 
5.4%
l 75685
 
4.1%
s 69312
 
3.7%
t 63611
 
3.4%
Other values (702) 772166
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1860342
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 164224
 
8.8%
e 148727
 
8.0%
143869
 
7.7%
i 112148
 
6.0%
n 106547
 
5.7%
o 103829
 
5.6%
r 100224
 
5.4%
l 75685
 
4.1%
s 69312
 
3.7%
t 63611
 
3.4%
Other values (702) 772166
41.5%
Distinct46589
Distinct (%)40.9%
Missing2
Missing (%)< 0.1%
Memory size1.7 MiB
2025-04-29T14:27:04.623157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length243
Median length145
Mean length20.116557
Min length1

Characters and Unicode

Total characters2293207
Distinct characters2084
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27956 ?
Unique (%)24.5%

Sample

1st rowComedy
2nd rowTo Begin Again
3rd rowCrazy Rich Asians (Original Motion Picture Soundtrack)
4th rowHold On
5th rowDays I Will Remember
ValueCountFrequency (%)
the 12029
 
3.1%
9198
 
2.3%
of 5240
 
1.3%
2022 3430
 
0.9%
vol 3256
 
0.8%
christmas 3214
 
0.8%
vivo 3186
 
0.8%
a 3174
 
0.8%
ao 2929
 
0.7%
de 2893
 
0.7%
Other values (35981) 343223
87.6%
2025-04-29T14:27:05.649630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
277776
 
12.1%
e 184971
 
8.1%
a 142801
 
6.2%
o 138419
 
6.0%
i 127744
 
5.6%
n 106155
 
4.6%
r 105848
 
4.6%
s 96725
 
4.2%
t 96375
 
4.2%
l 79065
 
3.4%
Other values (2074) 937328
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2293207
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
277776
 
12.1%
e 184971
 
8.1%
a 142801
 
6.2%
o 138419
 
6.0%
i 127744
 
5.6%
n 106155
 
4.6%
r 105848
 
4.6%
s 96725
 
4.2%
t 96375
 
4.2%
l 79065
 
3.4%
Other values (2074) 937328
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2293207
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
277776
 
12.1%
e 184971
 
8.1%
a 142801
 
6.2%
o 138419
 
6.0%
i 127744
 
5.6%
n 106155
 
4.6%
r 105848
 
4.6%
s 96725
 
4.2%
t 96375
 
4.2%
l 79065
 
3.4%
Other values (2074) 937328
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2293207
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
277776
 
12.1%
e 184971
 
8.1%
a 142801
 
6.2%
o 138419
 
6.0%
i 127744
 
5.6%
n 106155
 
4.6%
r 105848
 
4.6%
s 96725
 
4.2%
t 96375
 
4.2%
l 79065
 
3.4%
Other values (2074) 937328
40.9%
Distinct73608
Distinct (%)64.6%
Missing1
Missing (%)< 0.1%
Memory size1.7 MiB
2025-04-29T14:27:06.297375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length511
Median length146
Mean length17.994868
Min length1

Characters and Unicode

Total characters2051361
Distinct characters2417
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55711 ?
Unique (%)48.9%

Sample

1st rowComedy
2nd rowGhost - Acoustic
3rd rowTo Begin Again
4th rowCan't Help Falling In Love
5th rowHold On
ValueCountFrequency (%)
19654
 
5.1%
the 9471
 
2.5%
you 4292
 
1.1%
me 3716
 
1.0%
a 3696
 
1.0%
of 3605
 
0.9%
i 3409
 
0.9%
in 3180
 
0.8%
vivo 3158
 
0.8%
remix 2984
 
0.8%
Other values (50550) 328612
85.2%
2025-04-29T14:27:07.264923image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
271780
 
13.2%
e 174853
 
8.5%
a 136250
 
6.6%
o 122442
 
6.0%
i 109433
 
5.3%
n 94021
 
4.6%
r 92078
 
4.5%
t 81894
 
4.0%
s 67733
 
3.3%
l 63149
 
3.1%
Other values (2407) 837728
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2051361
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
271780
 
13.2%
e 174853
 
8.5%
a 136250
 
6.6%
o 122442
 
6.0%
i 109433
 
5.3%
n 94021
 
4.6%
r 92078
 
4.5%
t 81894
 
4.0%
s 67733
 
3.3%
l 63149
 
3.1%
Other values (2407) 837728
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2051361
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
271780
 
13.2%
e 174853
 
8.5%
a 136250
 
6.6%
o 122442
 
6.0%
i 109433
 
5.3%
n 94021
 
4.6%
r 92078
 
4.5%
t 81894
 
4.0%
s 67733
 
3.3%
l 63149
 
3.1%
Other values (2407) 837728
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2051361
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
271780
 
13.2%
e 174853
 
8.5%
a 136250
 
6.6%
o 122442
 
6.0%
i 109433
 
5.3%
n 94021
 
4.6%
r 92078
 
4.5%
t 81894
 
4.0%
s 67733
 
3.3%
l 63149
 
3.1%
Other values (2407) 837728
40.8%

popularity
Real number (ℝ)

Zeros 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.237934
Minimum0
Maximum100
Zeros16020
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-29T14:27:07.561981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median35
Q350
95-th percentile69
Maximum100
Range100
Interquartile range (IQR)33

Descriptive statistics

Standard deviation22.304812
Coefficient of variation (CV)0.67106495
Kurtosis-0.92771287
Mean33.237934
Median Absolute Deviation (MAD)16
Skewness0.046422211
Sum3789058
Variance497.50466
MonotonicityNot monotonic
2025-04-29T14:27:07.859905image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16020
 
14.1%
22 2354
 
2.1%
21 2344
 
2.1%
44 2288
 
2.0%
1 2140
 
1.9%
23 2117
 
1.9%
20 2110
 
1.9%
43 2073
 
1.8%
45 2004
 
1.8%
41 1996
 
1.8%
Other values (91) 78552
68.9%
ValueCountFrequency (%)
0 16020
14.1%
1 2140
 
1.9%
2 1036
 
0.9%
3 585
 
0.5%
4 389
 
0.3%
5 599
 
0.5%
6 426
 
0.4%
7 465
 
0.4%
8 544
 
0.5%
9 525
 
0.5%
ValueCountFrequency (%)
100 2
 
< 0.1%
99 1
 
< 0.1%
98 7
< 0.1%
97 8
< 0.1%
96 7
< 0.1%
95 5
< 0.1%
94 7
< 0.1%
93 12
< 0.1%
92 9
< 0.1%
91 10
< 0.1%

duration_ms
Real number (ℝ)

Distinct50697
Distinct (%)44.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228029.21
Minimum0
Maximum5237295
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-29T14:27:08.157316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile116920
Q1174066
median212906
Q3261506
95-th percentile387167.3
Maximum5237295
Range5237295
Interquartile range (IQR)87440

Descriptive statistics

Standard deviation107298.55
Coefficient of variation (CV)0.4705474
Kurtosis354.94748
Mean228029.21
Median Absolute Deviation (MAD)42760
Skewness11.195114
Sum2.5994874 × 1010
Variance1.1512979 × 1010
MonotonicityNot monotonic
2025-04-29T14:27:08.450989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162897 146
 
0.1%
180000 104
 
0.1%
192000 91
 
0.1%
240000 84
 
0.1%
118840 76
 
0.1%
172342 75
 
0.1%
227520 71
 
0.1%
131733 70
 
0.1%
243057 66
 
0.1%
175986 63
 
0.1%
Other values (50687) 113152
99.3%
ValueCountFrequency (%)
0 1
< 0.1%
8586 1
< 0.1%
13386 1
< 0.1%
15800 1
< 0.1%
17453 1
< 0.1%
17826 2
< 0.1%
21120 1
< 0.1%
21240 1
< 0.1%
22266 1
< 0.1%
23506 2
< 0.1%
ValueCountFrequency (%)
5237295 1
< 0.1%
4789026 2
< 0.1%
4730302 1
< 0.1%
4563897 1
< 0.1%
4447520 1
< 0.1%
4339826 1
< 0.1%
4334721 1
< 0.1%
4246206 1
< 0.1%
4120258 1
< 0.1%
3876276 2
< 0.1%

explicit
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1001.9 KiB
False
104251 
True
 
9747
ValueCountFrequency (%)
False 104251
91.4%
True 9747
 
8.6%
2025-04-29T14:27:08.678039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

danceability
Real number (ℝ)

Distinct1174
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56679126
Minimum0
Maximum0.985
Zeros158
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-29T14:27:08.916644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.456
median0.58
Q30.695
95-th percentile0.824
Maximum0.985
Range0.985
Interquartile range (IQR)0.239

Descriptive statistics

Standard deviation0.17354984
Coefficient of variation (CV)0.3061971
Kurtosis-0.18406504
Mean0.56679126
Median Absolute Deviation (MAD)0.119
Skewness-0.39963007
Sum64613.071
Variance0.030119548
MonotonicityNot monotonic
2025-04-29T14:27:09.225294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.647 431
 
0.4%
0.609 357
 
0.3%
0.579 347
 
0.3%
0.685 335
 
0.3%
0.602 334
 
0.3%
0.524 317
 
0.3%
0.689 315
 
0.3%
0.598 312
 
0.3%
0.607 307
 
0.3%
0.626 306
 
0.3%
Other values (1164) 110637
97.1%
ValueCountFrequency (%)
0 158
0.1%
0.0513 1
 
< 0.1%
0.0532 1
 
< 0.1%
0.0545 1
 
< 0.1%
0.0548 1
 
< 0.1%
0.055 1
 
< 0.1%
0.0555 1
 
< 0.1%
0.0558 1
 
< 0.1%
0.0562 1
 
< 0.1%
0.0565 2
 
< 0.1%
ValueCountFrequency (%)
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 1
 
< 0.1%
0.982 1
 
< 0.1%
0.981 2
< 0.1%
0.98 2
< 0.1%
0.979 2
< 0.1%
0.978 3
< 0.1%
0.977 1
 
< 0.1%
0.976 4
< 0.1%

energy
Real number (ℝ)

High correlation 

Distinct2083
Distinct (%)1.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.64139246
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-29T14:27:09.518309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.154
Q10.472
median0.685
Q30.854
95-th percentile0.969
Maximum1
Range1
Interquartile range (IQR)0.382

Descriptive statistics

Standard deviation0.25152426
Coefficient of variation (CV)0.39215344
Kurtosis-0.525605
Mean0.64139246
Median Absolute Deviation (MAD)0.186
Skewness-0.5970469
Sum73116.816
Variance0.063264453
MonotonicityNot monotonic
2025-04-29T14:27:09.821158image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.876 318
 
0.3%
0.937 269
 
0.2%
0.931 261
 
0.2%
0.886 258
 
0.2%
0.801 258
 
0.2%
0.858 254
 
0.2%
0.961 254
 
0.2%
0.948 254
 
0.2%
0.92 240
 
0.2%
0.981 238
 
0.2%
Other values (2073) 111393
97.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1.95 × 10-51
 
< 0.1%
2.01 × 10-513
 
< 0.1%
2.02 × 10-54
 
< 0.1%
2.03 × 10-534
< 0.1%
2.82 × 10-51
 
< 0.1%
3.05 × 10-51
 
< 0.1%
3.61 × 10-51
 
< 0.1%
4.28 × 10-53
 
< 0.1%
5.9 × 10-52
 
< 0.1%
ValueCountFrequency (%)
1 28
 
< 0.1%
0.999 100
0.1%
0.998 149
0.1%
0.997 165
0.1%
0.996 159
0.1%
0.995 229
0.2%
0.994 173
0.2%
0.993 184
0.2%
0.992 161
0.1%
0.991 200
0.2%

key
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.309166
Minimum0
Maximum11
Zeros13060
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-29T14:27:10.056511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5599656
Coefficient of variation (CV)0.67053197
Kurtosis-1.2765552
Mean5.309166
Median Absolute Deviation (MAD)3
Skewness-0.0085109577
Sum605229
Variance12.673355
MonotonicityNot monotonic
2025-04-29T14:27:10.277839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 13245
11.6%
0 13060
11.5%
2 11644
10.2%
9 11313
9.9%
1 10772
9.4%
5 9368
8.2%
11 9282
8.1%
4 9008
7.9%
6 7921
6.9%
10 7455
6.5%
Other values (2) 10929
9.6%
ValueCountFrequency (%)
0 13060
11.5%
1 10772
9.4%
2 11644
10.2%
3 3569
 
3.1%
4 9008
7.9%
5 9368
8.2%
6 7921
6.9%
7 13245
11.6%
8 7360
6.5%
9 11313
9.9%
ValueCountFrequency (%)
11 9282
8.1%
10 7455
6.5%
9 11313
9.9%
8 7360
6.5%
7 13245
11.6%
6 7921
6.9%
5 9368
8.2%
4 9008
7.9%
3 3569
 
3.1%
2 11644
10.2%

loudness
Real number (ℝ)

High correlation 

Distinct19480
Distinct (%)17.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-8.2588691
Minimum-49.531
Maximum4.532
Zeros0
Zeros (%)0.0%
Negative113907
Negative (%)99.9%
Memory size1.7 MiB
2025-04-29T14:27:10.543122image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-49.531
5-th percentile-18.0646
Q1-10.013
median-7.004
Q3-5.003
95-th percentile-2.974
Maximum4.532
Range54.063
Interquartile range (IQR)5.01

Descriptive statistics

Standard deviation5.0293183
Coefficient of variation (CV)-0.60895969
Kurtosis5.8966594
Mean-8.2588691
Median Absolute Deviation (MAD)2.343
Skewness-2.0066056
Sum-941486.3
Variance25.294043
MonotonicityNot monotonic
2025-04-29T14:27:11.082891image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.662 176
 
0.2%
-4.457 90
 
0.1%
-9.336 86
 
0.1%
-7.57 77
 
0.1%
-4.034 75
 
0.1%
-8.871 74
 
0.1%
-3.725 72
 
0.1%
-4.324 70
 
0.1%
-12.472 64
 
0.1%
-5.08 64
 
0.1%
Other values (19470) 113149
99.3%
ValueCountFrequency (%)
-49.531 1
 
< 0.1%
-49.307 1
 
< 0.1%
-46.591 1
 
< 0.1%
-46.251 1
 
< 0.1%
-43.957 1
 
< 0.1%
-43.943 1
 
< 0.1%
-43.714 1
 
< 0.1%
-43.504 1
 
< 0.1%
-43.303 1
 
< 0.1%
-43.046 3
< 0.1%
ValueCountFrequency (%)
4.532 1
< 0.1%
3.156 1
< 0.1%
2.574 1
< 0.1%
1.864 1
< 0.1%
1.821 1
< 0.1%
1.795 1
< 0.1%
1.7 1
< 0.1%
1.682 1
< 0.1%
1.673 1
< 0.1%
1.416 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size1.7 MiB
1.0
72679 
0.0
41318 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters341991
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 72679
63.8%
0.0 41318
36.2%
(Missing) 1
 
< 0.1%

Length

2025-04-29T14:27:11.336694image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-29T14:27:11.514228image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 72679
63.8%
0.0 41318
36.2%

Most occurring characters

ValueCountFrequency (%)
0 155315
45.4%
. 113997
33.3%
1 72679
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 341991
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 155315
45.4%
. 113997
33.3%
1 72679
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 341991
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 155315
45.4%
. 113997
33.3%
1 72679
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 341991
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 155315
45.4%
. 113997
33.3%
1 72679
21.3%

speechiness
Real number (ℝ)

Distinct1489
Distinct (%)1.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.084653364
Minimum0
Maximum0.965
Zeros157
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-29T14:27:11.744790image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0282
Q10.0359
median0.0489
Q30.0845
95-th percentile0.268
Maximum0.965
Range0.965
Interquartile range (IQR)0.0486

Descriptive statistics

Standard deviation0.10573347
Coefficient of variation (CV)1.2490167
Kurtosis28.823665
Mean0.084653364
Median Absolute Deviation (MAD)0.0165
Skewness4.6474599
Sum9650.2295
Variance0.011179566
MonotonicityNot monotonic
2025-04-29T14:27:12.053906image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0323 400
 
0.4%
0.0324 376
 
0.3%
0.0322 373
 
0.3%
0.0328 363
 
0.3%
0.0295 358
 
0.3%
0.0321 352
 
0.3%
0.033 347
 
0.3%
0.0367 346
 
0.3%
0.0326 340
 
0.3%
0.0363 332
 
0.3%
Other values (1479) 110410
96.9%
ValueCountFrequency (%)
0 157
0.1%
0.0221 3
 
< 0.1%
0.0222 1
 
< 0.1%
0.0223 3
 
< 0.1%
0.0225 2
 
< 0.1%
0.0226 2
 
< 0.1%
0.0227 3
 
< 0.1%
0.0228 5
 
< 0.1%
0.0229 1
 
< 0.1%
0.023 9
 
< 0.1%
ValueCountFrequency (%)
0.965 1
 
< 0.1%
0.963 2
 
< 0.1%
0.962 6
< 0.1%
0.961 2
 
< 0.1%
0.96 3
 
< 0.1%
0.959 6
< 0.1%
0.958 6
< 0.1%
0.957 8
< 0.1%
0.956 7
< 0.1%
0.955 11
< 0.1%

acousticness
Real number (ℝ)

High correlation 

Distinct5061
Distinct (%)4.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.31490198
Minimum0
Maximum0.996
Zeros39
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-29T14:27:12.363338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.000145
Q10.0169
median0.169
Q30.597
95-th percentile0.948
Maximum0.996
Range0.996
Interquartile range (IQR)0.5801

Descriptive statistics

Standard deviation0.33252117
Coefficient of variation (CV)1.0559513
Kurtosis-0.94984313
Mean0.31490198
Median Absolute Deviation (MAD)0.1675
Skewness0.7273458
Sum35897.881
Variance0.11057033
MonotonicityNot monotonic
2025-04-29T14:27:12.667882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 305
 
0.3%
0.993 267
 
0.2%
0.994 266
 
0.2%
0.992 250
 
0.2%
0.991 218
 
0.2%
0.131 206
 
0.2%
0.881 204
 
0.2%
0.108 195
 
0.2%
0.107 190
 
0.2%
0.99 189
 
0.2%
Other values (5051) 111707
98.0%
ValueCountFrequency (%)
0 39
< 0.1%
1 × 10-61
 
< 0.1%
1.01 × 10-64
 
< 0.1%
1.02 × 10-61
 
< 0.1%
1.03 × 10-62
 
< 0.1%
1.04 × 10-64
 
< 0.1%
1.06 × 10-65
 
< 0.1%
1.07 × 10-64
 
< 0.1%
1.08 × 10-62
 
< 0.1%
1.09 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.996 103
 
0.1%
0.995 305
0.3%
0.994 266
0.2%
0.993 267
0.2%
0.992 250
0.2%
0.991 218
0.2%
0.99 189
0.2%
0.989 177
0.2%
0.988 150
0.1%
0.987 158
0.1%

instrumentalness
Real number (ℝ)

Zeros 

Distinct5346
Distinct (%)4.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.1560537
Minimum0
Maximum1
Zeros38761
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-29T14:27:12.972310image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.16 × 10-5
Q30.049
95-th percentile0.904
Maximum1
Range1
Interquartile range (IQR)0.049

Descriptive statistics

Standard deviation0.30955789
Coefficient of variation (CV)1.9836626
Kurtosis1.2705981
Mean0.1560537
Median Absolute Deviation (MAD)4.16 × 10-5
Skewness1.7343643
Sum17789.653
Variance0.095826085
MonotonicityNot monotonic
2025-04-29T14:27:13.273323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38761
34.0%
3.59 × 10-5166
 
0.1%
0.905 122
 
0.1%
0.895 122
 
0.1%
0.934 121
 
0.1%
0.922 118
 
0.1%
0.000141 115
 
0.1%
0.911 115
 
0.1%
0.913 114
 
0.1%
0.9 114
 
0.1%
Other values (5336) 74129
65.0%
ValueCountFrequency (%)
0 38761
34.0%
1 × 10-632
 
< 0.1%
1.01 × 10-646
 
< 0.1%
1.02 × 10-636
 
< 0.1%
1.03 × 10-634
 
< 0.1%
1.04 × 10-650
 
< 0.1%
1.05 × 10-639
 
< 0.1%
1.06 × 10-649
 
< 0.1%
1.07 × 10-656
 
< 0.1%
1.08 × 10-647
 
< 0.1%
ValueCountFrequency (%)
1 13
< 0.1%
0.999 22
< 0.1%
0.998 6
 
< 0.1%
0.997 11
< 0.1%
0.996 4
 
< 0.1%
0.995 15
< 0.1%
0.994 4
 
< 0.1%
0.993 9
< 0.1%
0.992 11
< 0.1%
0.991 12
< 0.1%

liveness
Real number (ℝ)

Distinct1722
Distinct (%)1.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.21355544
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-29T14:27:13.562503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0606
Q10.098
median0.132
Q30.273
95-th percentile0.681
Maximum1
Range1
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.19037947
Coefficient of variation (CV)0.89147562
Kurtosis4.3780692
Mean0.21355544
Median Absolute Deviation (MAD)0.051
Skewness2.1056981
Sum24344.68
Variance0.036244344
MonotonicityNot monotonic
2025-04-29T14:27:13.871711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.108 1353
 
1.2%
0.111 1318
 
1.2%
0.109 1198
 
1.1%
0.11 1179
 
1.0%
0.105 1114
 
1.0%
0.107 1101
 
1.0%
0.103 1094
 
1.0%
0.106 1064
 
0.9%
0.112 1063
 
0.9%
0.113 1008
 
0.9%
Other values (1712) 102505
89.9%
ValueCountFrequency (%)
0 2
< 0.1%
0.00925 1
< 0.1%
0.00986 1
< 0.1%
0.0112 1
< 0.1%
0.0114 1
< 0.1%
0.0116 1
< 0.1%
0.0118 1
< 0.1%
0.0133 1
< 0.1%
0.0136 1
< 0.1%
0.0137 1
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0.997 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 3
 
< 0.1%
0.993 2
 
< 0.1%
0.992 9
< 0.1%
0.991 4
 
< 0.1%
0.99 11
< 0.1%
0.989 17
< 0.1%
0.988 17
< 0.1%

valence
Real number (ℝ)

Distinct1790
Distinct (%)1.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.47406667
Minimum0
Maximum0.995
Zeros176
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-29T14:27:14.161116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0708
Q10.26
median0.464
Q30.683
95-th percentile0.911
Maximum0.995
Range0.995
Interquartile range (IQR)0.423

Descriptive statistics

Standard deviation0.25926313
Coefficient of variation (CV)0.54689171
Kurtosis-1.0274425
Mean0.47406667
Median Absolute Deviation (MAD)0.212
Skewness0.11509317
Sum54042.178
Variance0.067217373
MonotonicityNot monotonic
2025-04-29T14:27:14.474715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 300
 
0.3%
0.304 248
 
0.2%
0.717 233
 
0.2%
0.962 230
 
0.2%
0.324 225
 
0.2%
0.963 216
 
0.2%
0.55 210
 
0.2%
0.365 205
 
0.2%
0.949 204
 
0.2%
0.464 201
 
0.2%
Other values (1780) 111725
98.0%
ValueCountFrequency (%)
0 176
0.2%
1 × 10-5129
0.1%
0.000322 1
 
< 0.1%
0.000378 1
 
< 0.1%
0.000667 1
 
< 0.1%
0.000673 1
 
< 0.1%
0.000755 1
 
< 0.1%
0.000781 1
 
< 0.1%
0.00084 1
 
< 0.1%
0.000885 1
 
< 0.1%
ValueCountFrequency (%)
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.993 3
< 0.1%
0.992 4
< 0.1%
0.991 3
< 0.1%
0.99 1
 
< 0.1%
0.989 1
 
< 0.1%
0.988 4
< 0.1%
0.987 2
< 0.1%
0.986 1
 
< 0.1%

tempo
Real number (ℝ)

Distinct45653
Distinct (%)40.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean122.14816
Minimum0
Maximum243.372
Zeros157
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-04-29T14:27:14.775140image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.3466
Q199.219
median122.017
Q3140.071
95-th percentile175.0676
Maximum243.372
Range243.372
Interquartile range (IQR)40.852

Descriptive statistics

Standard deviation29.978418
Coefficient of variation (CV)0.24542669
Kurtosis-0.10860943
Mean122.14816
Median Absolute Deviation (MAD)21.703
Skewness0.23227357
Sum13924524
Variance898.70554
MonotonicityNot monotonic
2025-04-29T14:27:15.297722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 157
 
0.1%
151.925 146
 
0.1%
95.004 95
 
0.1%
130.594 76
 
0.1%
87.925 76
 
0.1%
125.004 70
 
0.1%
92.988 70
 
0.1%
76.783 69
 
0.1%
77.321 67
 
0.1%
90.04 63
 
0.1%
Other values (45643) 113108
99.2%
ValueCountFrequency (%)
0 157
0.1%
30.2 1
 
< 0.1%
30.322 1
 
< 0.1%
31.834 1
 
< 0.1%
34.262 1
 
< 0.1%
34.821 1
 
< 0.1%
35.392 1
 
< 0.1%
35.79 1
 
< 0.1%
35.862 1
 
< 0.1%
35.928 1
 
< 0.1%
ValueCountFrequency (%)
243.372 1
 
< 0.1%
222.605 1
 
< 0.1%
220.525 1
 
< 0.1%
220.084 1
 
< 0.1%
220.081 3
< 0.1%
220.039 1
 
< 0.1%
219.971 1
 
< 0.1%
219.693 1
 
< 0.1%
219.571 1
 
< 0.1%
218.879 1
 
< 0.1%

time_signature
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size1.7 MiB
4.0
101840 
3.0
 
9195
5.0
 
1826
1.0
 
973
0.0
 
163

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters341991
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 101840
89.3%
3.0 9195
 
8.1%
5.0 1826
 
1.6%
1.0 973
 
0.9%
0.0 163
 
0.1%
(Missing) 1
 
< 0.1%

Length

2025-04-29T14:27:15.618719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-29T14:27:15.856275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
4.0 101840
89.3%
3.0 9195
 
8.1%
5.0 1826
 
1.6%
1.0 973
 
0.9%
0.0 163
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 114160
33.4%
. 113997
33.3%
4 101840
29.8%
3 9195
 
2.7%
5 1826
 
0.5%
1 973
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 341991
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 114160
33.4%
. 113997
33.3%
4 101840
29.8%
3 9195
 
2.7%
5 1826
 
0.5%
1 973
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 341991
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 114160
33.4%
. 113997
33.3%
4 101840
29.8%
3 9195
 
2.7%
5 1826
 
0.5%
1 973
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 341991
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 114160
33.4%
. 113997
33.3%
4 101840
29.8%
3 9195
 
2.7%
5 1826
 
0.5%
1 973
 
0.3%
Distinct114
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size1.7 MiB
2025-04-29T14:27:16.378587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length11
Mean length7.070151
Min length3

Characters and Unicode

Total characters805976
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowacoustic
2nd rowacoustic
3rd rowacoustic
4th rowacoustic
5th rowacoustic
ValueCountFrequency (%)
afrobeat 1000
 
0.9%
alternative 1000
 
0.9%
alt-rock 1000
 
0.9%
breakbeat 1000
 
0.9%
british 1000
 
0.9%
ambient 1000
 
0.9%
anime 1000
 
0.9%
black-metal 1000
 
0.9%
bluegrass 1000
 
0.9%
blues 1000
 
0.9%
Other values (104) 103997
91.2%
2025-04-29T14:27:17.189688image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 73000
 
9.1%
a 67997
 
8.4%
o 66997
 
8.3%
r 57000
 
7.1%
n 50000
 
6.2%
i 46997
 
5.8%
s 43997
 
5.5%
t 42997
 
5.3%
l 39000
 
4.8%
p 39000
 
4.8%
Other values (15) 278991
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 805976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 73000
 
9.1%
a 67997
 
8.4%
o 66997
 
8.3%
r 57000
 
7.1%
n 50000
 
6.2%
i 46997
 
5.8%
s 43997
 
5.5%
t 42997
 
5.3%
l 39000
 
4.8%
p 39000
 
4.8%
Other values (15) 278991
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 805976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 73000
 
9.1%
a 67997
 
8.4%
o 66997
 
8.3%
r 57000
 
7.1%
n 50000
 
6.2%
i 46997
 
5.8%
s 43997
 
5.5%
t 42997
 
5.3%
l 39000
 
4.8%
p 39000
 
4.8%
Other values (15) 278991
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 805976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 73000
 
9.1%
a 67997
 
8.4%
o 66997
 
8.3%
r 57000
 
7.1%
n 50000
 
6.2%
i 46997
 
5.8%
s 43997
 
5.5%
t 42997
 
5.3%
l 39000
 
4.8%
p 39000
 
4.8%
Other values (15) 278991
34.6%

Interactions

2025-04-29T14:26:55.527461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:22.252244image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:26.181648image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:28.974679image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:31.828822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:34.952763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:37.520540image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:40.422913image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:43.386000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:46.505851image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:49.341753image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:52.043201image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:55.740446image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:22.495306image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:26.410136image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:29.202048image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:32.028734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:35.152722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:37.730290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:40.629923image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:43.595235image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:46.725382image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:49.558088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:52.420345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:55.967698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:22.960073image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:26.788309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:29.418275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:32.258099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:35.389366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:38.166429image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:40.852882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:43.817296image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:46.948172image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:49.781944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:52.696297image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:56.195318image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:23.203776image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:27.035621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:29.636235image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:32.469060image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:35.597635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:38.390820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:41.094694image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:44.030408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:47.168294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:50.001541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:52.909964image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:56.416496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:23.444387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:27.257579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:29.868666image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:32.689773image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:35.815008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:38.620460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:41.316114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:44.476554image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:47.378517image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:50.243131image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:53.122327image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:56.635339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:23.668162image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:27.461438image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:30.092014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:32.892726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:36.017332image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:38.841614image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:41.725772image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:44.690213image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:47.590206image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:50.461147image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:53.342106image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:56.848915image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:24.073256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:27.679346image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:30.313869image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:33.119593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:36.242986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:39.057420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:41.952244image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:44.986580image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:47.807130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:50.688381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:53.558918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:57.065697image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:24.364377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:27.898625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:30.669844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:33.340358image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:36.450378image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:39.299164image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:42.191036image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:45.385229image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:48.014142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:50.928693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:53.771872image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:57.276987image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:24.649415image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:28.109031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:30.961671image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:33.556263image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:36.667297image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:39.522633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:42.405954image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:45.617908image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:48.231363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:51.155961image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:53.989812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:57.503458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:24.948417image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:28.335849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:31.185853image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:33.780225image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:36.883865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:39.757777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:42.631869image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:45.852087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:48.451398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:51.378182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:54.443101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:57.715292image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:25.269233image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:28.546937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:31.394691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:33.999563image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:37.095031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:39.981423image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:42.840535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:46.059076image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:48.815128image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:51.589211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:54.661849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:57.934980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:25.884227image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:28.766152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:31.610436image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:34.731571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:37.309337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:40.210123image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:43.161421image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:46.289674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:49.110402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:51.813956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-29T14:26:55.208073image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-04-29T14:27:17.527955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
acousticnessdanceabilityduration_msenergyexplicitinstrumentalnesskeylivenessloudnessmodepopularityspeechinesstempotime_signaturevalence
acousticness1.000-0.039-0.170-0.7080.102-0.096-0.038-0.042-0.5340.1000.008-0.214-0.2170.141-0.021
danceability-0.0391.000-0.0980.0390.154-0.1440.035-0.1450.1120.0850.0270.159-0.0710.2790.462
duration_ms-0.170-0.0981.0000.1040.0110.1270.014-0.0400.0220.0040.028-0.1290.0500.036-0.178
energy-0.7080.0390.1041.0000.116-0.0350.0450.1770.7500.087-0.0230.3550.2410.1610.208
explicit0.1020.1540.0110.1161.0000.1040.0400.0420.1080.0370.0890.3060.0400.0600.069
instrumentalness-0.096-0.1440.127-0.0350.1041.0000.005-0.099-0.2890.059-0.078-0.049-0.0050.067-0.320
key-0.0380.0350.0140.0450.0400.0051.000-0.0040.0320.247-0.0030.0440.0120.0210.033
liveness-0.042-0.145-0.0400.1770.042-0.099-0.0041.0000.1110.029-0.0080.0920.0190.0400.013
loudness-0.5340.1120.0220.7500.108-0.2890.0320.1111.0000.0450.0350.2320.1940.1520.221
mode0.1000.0850.0040.0870.0370.0590.2470.0290.0451.0000.0370.0670.0260.0280.033
popularity0.0080.0270.028-0.0230.089-0.078-0.003-0.0080.0350.0371.000-0.0680.0170.046-0.042
speechiness-0.2140.159-0.1290.3550.306-0.0490.0440.0920.2320.067-0.0681.0000.1150.0850.092
tempo-0.217-0.0710.0500.2410.040-0.0050.0120.0190.1940.0260.0170.1151.0000.4960.063
time_signature0.1410.2790.0360.1610.0600.0670.0210.0400.1520.0280.0460.0850.4961.0000.111
valence-0.0210.462-0.1780.2080.069-0.3200.0330.0130.2210.033-0.0420.0920.0630.1111.000

Missing values

2025-04-29T14:26:58.281008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-29T14:26:59.063893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-29T14:27:00.100083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

track_urlartistsalbum_nametrack_namepopularityduration_msexplicitdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signaturetrack_genre
track_id
05SuOikwiRyPMVoIQDJUgSVGen HoshinoComedyComedy73230666False0.6760.46101.0-6.7460.00.14300.03220.0000010.35800.715087.9174.0acoustic
14qPNDBW1i3p13qLCt0Ki3ABen WoodwardNaNGhost - Acoustic55149610False0.4200.16601.0-17.2351.00.07630.92400.0000060.10100.267077.4894.0acoustic
21iJBSr7s7jYXzM8EGcbK5bIngrid Michaelson;ZAYNTo Begin AgainTo Begin Again57210826False0.4380.35900.0-9.7341.00.05570.21000.0000000.11700.120076.3324.0acoustic
36lfxq3CG4xtTiEg7opyCyxKina GrannisCrazy Rich Asians (Original Motion Picture Soundtrack)Can't Help Falling In Love71201933False0.2660.05960.0-18.5151.00.03630.90500.0000710.13200.1430181.7403.0acoustic
45vjLSffimiIP26QG5WcN2KChord OverstreetHold OnHold On82198853False0.6180.44302.0-9.6811.00.05260.46900.0000000.08290.1670119.9494.0acoustic
501MVOl9KtVTNfFiBU9I7dcTyrone WellsDays I Will RememberDays I Will Remember58214240False0.6880.48106.0-8.8071.00.10500.28900.0000000.18900.666098.0174.0acoustic
66Vc5wAMmXdKIAM7WUoEb7NA Great Big World;Christina AguileraIs There Anybody Out There?Say Something74229400False0.4070.14702.0-8.8221.00.03550.85700.0000030.09130.0765141.2843.0acoustic
71EzrEOXmMH3G43AXT1y7pAJason MrazWe Sing. We Dance. We Steal Things.I'm Yours80242946False0.7030.444011.0-9.3311.00.04170.55900.0000000.09730.7120150.9604.0acoustic
80IktbUcnAGrvD03AWnz3Q8Jason Mraz;Colbie CaillatWe Sing. We Dance. We Steal Things.Lucky74189613False0.6250.41400.0-8.7001.00.03690.29400.0000000.15100.6690130.0884.0acoustic
97k9GuJYLp2AzqokyEdwEw2Ross CoppermanHungerHunger56205594False0.4420.63201.0-6.7701.00.02950.42600.0041900.07350.196078.8994.0acoustic
track_urlartistsalbum_nametrack_namepopularityduration_msexplicitdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signaturetrack_genre
track_id
1139902A4dSiJmbviL56CBupkh6CLucas CervettiFrecuencias Álmicas en 432hz (Solo Piano)Frecuencia Álmica XI - Solo Piano22369049False0.5790.2454.0-16.3571.00.03840.970000.9240000.10100.3020112.0113.0world-music
1139910CE0Y6GM75cbrqao8EOAlWChris TomlinThe Ultimate PlaylistAt The Cross (Love Ran Red)32250629False0.3870.5318.0-4.7881.00.02900.003050.0000000.20100.1530146.0034.0world-music
1139923FjOBB4EyIXHYUtSgrIdY9Jesus CultureRevelation SongsYour Love Never Fails38312566False0.4750.86010.0-4.7221.00.04210.006500.0000020.24600.4270113.9494.0world-music
1139934OkMK49i3NApR1KsAIsTf6Chris TomlinSee The Morning (Special Edition)How Can I Keep From Singing39256026False0.5050.68710.0-4.3751.00.02870.084100.0000000.18800.3820104.0833.0world-music
1139944WbOUe6T0sozC7z5ZJgiAALucas CervettiFrecuencias Álmicas en 432hzFrecuencia Álmica, Pt. 422305454False0.3310.1711.0-15.6681.00.03500.920000.0229000.06790.3270132.1473.0world-music
1139952C3TZjDRiAzdyViavDJ217Rainy Lullaby#mindfulness - Soft Rain for Mindful Meditation, Stress Relief Relaxation MusicSleep My Little Boy21384999False0.1720.2355.0-16.3931.00.04220.640000.9280000.08630.0339125.9955.0world-music
1139961hIz5L4IB9hN3WRYPOCGPwRainy Lullaby#mindfulness - Soft Rain for Mindful Meditation, Stress Relief Relaxation MusicWater Into Light22385000False0.1740.1170.0-18.3180.00.04010.994000.9760000.10500.035085.2394.0world-music
1139976x8ZfSoqDjuNa5SVP5QjvXCesária EvoraBest OfMiss Perfumado22271466False0.6290.3290.0-10.8950.00.04200.867000.0000000.08390.7430132.3784.0world-music
1139982e6sXL2bYv4bSz6VTdnfLsMichael W. SmithChange Your WorldFriends41283893False0.5870.5067.0-10.8891.00.02970.381000.0000000.27000.4130135.9604.0world-music
1139992hETkH7cOfqmz3LqZDHZf5Cesária EvoraMiss PerfumadoBarbincor22241826False0.5260.4871.0-10.2040.00.07250.681000.0000000.08930.708079.1984.0world-music

Duplicate rows

Most frequently occurring

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350jI5ex80hYYbLQsX3G5Ze7Hans Zimmer;Henning Lohner;Martin Tillman;Fiachra TrenchHans Zimmer: Epic ScoresThe Well12684626False0.2350.09759.0-25.2170.00.03450.94600.8280000.10700.0361145.0333.0german3
1362abeQBWSIzjO8J1KrPFZHyOleg PogudinРусский романс. Часть IЗабыли вы…0190933False0.4740.05387.0-21.5410.00.04880.91200.0000000.09010.207093.9643.0romance3
27252VSOpFbg4GBpPNNv4ulbPГруппа "Загадка"20 золотых дворовых песен. РазлукаВ больнице больная лежала0196280False0.3630.15402.0-10.6300.00.03190.90100.0000000.14000.4110143.5213.0romance3
3225sP7Jb0FBF2fatb1CZ3zpaHans Zimmer;Lisa Gerrard;Gavin Greenaway;The Lyndhurst OrchestraHans Zimmer: Epic ScoresThe Gladiator Waltz - From "Gladiator" Soundtrack12505200False0.3390.42908.0-15.4411.00.04720.15900.9030000.10900.2380171.4953.0german3
3536IzTjoU9t5DiBN4BmiWBB8Hans Zimmer;Henning Lohner;Martin Tillman;Fiachra TrenchHans Zimmer: Epic ScoresShe Never Sleeps24137400False0.3580.13602.0-19.8011.00.03750.00190.8520000.25300.072872.6614.0german3
3586P2z6tZi1K8pyHXV1i0DnKNikolay KopylovУ камина (Старинные Русские Романсы)/ At a FireplaceТвои глаза зеленые ( Green Eyes)0219880False0.3590.44802.0-7.9611.00.06300.86900.0004600.31700.490076.8324.0romance3
000JZ83w0Qm09f4PwWj06sMGeorge JonesWith LoveA Good Year For The Roses12190546False0.4910.334011.0-9.6841.00.02870.65900.0000160.11600.249091.6744.0honky-tonk2
102KmEChUwcjxG3G29kbLFTHans Zimmer;Henning Lohner;Martin Tillman;Fiachra TrenchHans Zimmer: Epic ScoresShelter Mountain16250520False0.1440.26209.0-21.2281.00.06410.87600.7550000.14400.034594.4304.0german2
202MRylJ1WAgxzdqfNfdIsROleg PogudinЛюбовь и разлука. Песни Исаака ШварцаГород пышный, город бедный0114600False0.4460.12104.0-12.7370.00.04140.93700.0000000.12500.1800107.5733.0romance2
303B8ZXUmuDpf59j5PIMFHqWolfgang Amadeus Mozart;Heinz Holliger;Academy of St. Martin in the Fields;Kenneth SillitoMozart - All Day ClassicsMa che vi fece... Sperai vicino, K.368: 1. Andantino786160False0.2900.04905.0-24.0721.00.05550.98000.7530000.10800.114076.4734.0classical2